What the team does

Recart runs on data. The CEO uses it to size enterprise opportunities. The Head of CS uses it to defend accounts from churn. The Head of Product uses it to scope features. The Data team makes all of that possible, by building reliable pipelines, maintaining dashboards, and going deep on analytical questions that nobody else has the time or tools to answer.

This isn't a team that produces static reports. On any given week that means: building a predictive cohort model to forecast cross-subscriber rates 18–24 months out (in Hex), migrating the orders table to SingleStore to unblock analytical query performance, debugging a discrepancy between Shopify's web UI and the database because the CEO is asking why the numbers don't match.

What they work on

• Dashboards & reporting - building and maintaining Looker dashboards across SMS performance, opt-in metrics, campaign CTR, revenue recovery, GMV, subscriber trends, and merchant-level margin analysis
• Predictive analytics  cohort-based cross-subscriber and cross-purchase prediction models (delivered via Hex apps), identity resolution, and subscriber behavior analysis
• Data infrastructure - SingleStore migration (analytical query performance), Estuary pipelines (syncing Postgres, HubSpot, and other sources), dbt transformation layer, Postgres data warehouse management
• Ad-hoc analysis - answering CEO-level questions with same-day turnaround: Shop Pay penetration trends, SMS campaign performance benchmarks, subscriber volume discrepancies, merchant-level ROI breakdowns

Tech they work with

• BI & analytics: Looker, Hex
• Databases: Postgres, SingleStore, MongoDB
• Data pipelines: Estuary, dbt
• Other: HubSpot, Stripe, Shopify data, GitHub

How our engineering teams work

On any given day, our engineers are fielding questions from CS, syncing with Product, and reviewing each other's scopes on Notion before a single line of code is written. We do standups, run design reviews, and write internal docs when something new gets built, so knowledge doesn't live only in Slack threads.

We move fast, but we're deliberate: new features go through feature flags, changes are scoped before they're picked up, and on-call rotations mean someone's always got the weekend covered.

What kind of problems they solve

"We're outgrowing Postgres for analytical queries. Plan and execute the orders table migration to SingleStore without disrupting production."

"Looker and Shopify show different order counts for the same period. Figure out why. They're both right, just measuring different things."

"Build a cross-subscriber prediction model that lets leadership see 18 months into the future on network growth."

Who thrives here

You'll do well here if you're equally comfortable writing SQL, debugging data pipelines, and presenting findings to the CEO in a 10-minute async video. The Data team at Recart operates with a lot of autonomy, and a lot of accountability. When the numbers are wrong, you're the one who finds out why.

Comfort with ambiguity in data (conflicting metrics, evolving schemas, imperfect pipelines) is essential. So is the ability to communicate clearly with non-technical stakeholders who need answers fast.

Size & structure

1–2 Growth Engineers, working closely with the CTO, CEO, Head of Product, and Head of CS.